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      <title>如何设计一个本地缓存 - 交互式测验</title>
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      </style>
    </head>
    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>如何设计一个本地缓存 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
                        <div id="progress-indicator"></div>
                    </div>
                    <div id="question-container">
                      <p id="question-text"></p>
                      <ul class="options-list" id="options-list"></ul>
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                    <div id="result-container"></div>
                    <div id="hint-container" class="hint-container"></div>
                </div>
                <div class="quiz-footer" id="quiz-footer">
                  <button id="prev-btn" class="nav-btn">上一题</button>
                  <button id="hint-btn" class="nav-btn" style="background-color: #ffc107; color: #333; box-shadow: 0 4px 12px rgba(255,193,7,0.3);">提示</button>
                  <button id="next-btn" class="nav-btn">下一题</button>
                </div>
            </div>
          </div>
      </div>

      <script>
        const quizData = [
  {
    "question": "根据文档，本地缓存技术的核心思想是什么，它旨在解决什么样的问题？",
    "options": [
      "时间换空间：通过增加计算时间来节省内存。",
      "空间换时间：通过使用内存空间预存数据，来减少访问慢速数据源的耗时。",
      "降低一致性：通过牺牲数据实时性来提高系统可用性。",
      "CPU换IO：通过增加CPU计算来减少磁盘读写操作。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档明确指出，本地缓存的核心思想是“空间换时间”。它通过将数据加载到访问速度极快的本地内存（空间）中，来避免每次都从较慢的外部数据源（如数据库或远程服务）获取数据，从而显著减少了访问延迟（时间），提升了系统性能。",
    "source": "文档中「本地缓存的基本概念」-「1. 本地缓存的核心思想」部分",
    "hint": "思考一下，将数据从硬盘或网络加载到内存中，是用什么资源换取了什么效率的提升？"
  },
  {
    "question": "文档指出了本地缓存的一些局限性。在分布式或多节点应用场景中，本地缓存面临的最主要挑战是什么？",
    "options": [
      "架构过于复杂，难以实现",
      "访问速度通常比分布式缓存慢",
      "难以保证多个节点间的数据一致性",
      "必须占用堆外内存，导致GC问题"
    ],
    "correctAnswerIndex": 2,
    "explanation": "本地缓存的数据存储在单个应用的内存中。在分布式系统中，每个应用实例都有自己的本地缓存副本。当源数据更新时，需要复杂的机制来同步所有节点的缓存，否则就会出现数据不一致的问题。这是本地缓存应用于分布式环境的主要局限。",
    "source": "文档中「本地缓存的基本概念」-「4. 本地缓存的局限性」部分",
    "hint": "当一个应用有多个实例（节点）在运行时，如果每个实例都有自己的独立缓存，当数据源更新时会发生什么情况？"
  },
  {
    "question": "假设一个场景：系统中有一些核心数据被长期频繁访问，但偶尔也会有大量临时数据被短暂访问。为了防止这些临时数据冲刷掉核心热点数据（即“缓存污染”），应该优先选择哪种缓存淘汰策略？",
    "options": [
      "LRU (最近最少使用策略)",
      "FIFO (先进先出策略)",
      "LFU (最少使用频率策略)",
      "随机淘汰策略"
    ],
    "correctAnswerIndex": 2,
    "explanation": "LFU策略基于访问频率来淘汰数据，它会淘汰访问次数最少的数据。因此，即使大量临时数据被短暂访问，它们的访问频率也远低于长期核心数据，从而保护了核心热点数据不被淘汰，有效防止了‘缓存污染’。而LRU在这种场景下，可能会因为临时数据的频繁访问而淘汰掉真正重要的旧数据。",
    "source": "文档中「缓存的淘汰策略」-「2. LFU（Least Frequently Used） - 最少使用频率策略」部分",
    "hint": "哪种淘汰策略更关注一个数据项被访问的“总次数”，而不是“最近一次”被访问的时间？"
  },
  {
    "question": "对于金融或订单管理这类对数据准确性要求极高的系统，文档推荐采用哪种缓存一致性策略来保证强一致性？",
    "options": [
      "写回 (Write-Behind) 策略",
      "写直达 (Write-Through) 策略",
      "基于时间的失效 (TTL) 策略",
      "缓存同步 (Cache Synchronization) 机制"
    ],
    "correctAnswerIndex": 1,
    "explanation": "写直达（Write-Through）策略要求每次写入数据时，必须先成功写入缓存，再成功写入底层存储。这个过程是同步的，确保了缓存和数据源的数据时刻保持一致，从而满足了金融等场景的强一致性要求。",
    "source": "文档中「缓存一致性」-「4. 不同场景下的一致性策略选择」部分",
    "hint": "文档中提到，某个策略会'先将数据写入缓存，再写入底层存储'，并且这种方式'可以保证缓存和存储之间的同步'。"
  },
  {
    "question": "在对本地缓存进行监控时，哪个关键指标最能直接反映缓存策略的有效性，即缓存是否存储了用户真正需要的数据？",
    "options": [
      "缓存命中率 (Cache Hit Ratio)",
      "缓存大小 (Cache Size)",
      "访问延迟 (Latency)",
      "缓存淘汰率 (Cache Eviction Rate)"
    ],
    "correctAnswerIndex": 0,
    "explanation": "缓存命中率是指从缓存中成功读取数据的请求占总请求的比例。高命中率意味着大部分请求都从缓存中得到了满足，避免了对后端数据源的访问，这直接证明了缓存策略是有效的，缓存中存储了正确的热点数据。",
    "source": "文档中「缓存的监控与管理」-「2. 监控的关键指标」部分",
    "hint": "衡量缓存‘有效性’的核心是看有多少请求可以直接从缓存中获得满足，这个比例被称为什么？"
  },
  {
    "question": "如果使用 Java 中的 `LinkedHashMap` 并通过其特性（如重写`removeEldestEntry`方法）来实现一个简单的固定大小缓存，那么它实现的是哪种缓存淘汰策略？",
    "options": [
      "LFU (最少使用频率)",
      "FIFO (先进先出)",
      "ARC (自适应替换)",
      "LRU (最近最少使用)"
    ],
    "correctAnswerIndex": 3,
    "explanation": "文档中明确提到，`LinkedHashMap` 在 `HashMap` 的基础上增加了一个双向链表来维护插入顺序或访问顺序。通过设置 `accessOrder` 参数并重写 `removeEldestEntry` 方法，可以轻松实现一个 LRU（最近最少使用）缓存，因为它能自动淘汰最久未被访问的条目。",
    "source": "文档中「缓存设计的关键要素」-「1. 缓存的数据结构选择」部分",
    "hint": "`LinkedHashMap`可以通过一个构造参数来决定其内部链表是按插入顺序还是访问顺序排序。后者是实现哪种经典淘汰算法的关键？"
  },
  {
    "question": "文档中介绍了一种能够结合LRU和LFU思想，并根据实时访问模式动态调整策略的复杂缓存淘汰算法。这种算法是什么？",
    "options": [
      "ARC (自适应替换缓存策略)",
      "FIFO (先进先出策略)",
      "随机淘汰策略",
      "TTI (Time to Idle) 策略"
    ],
    "correctAnswerIndex": 0,
    "explanation": "ARC (Adaptive Replacement Cache) 是一种自适应替换缓存策略，它通过维护两个LRU队列（一个用于最近访问，一个用于频繁访问）并动态调整它们的大小，来同时兼顾时间局部性和频率局部性，从而适应多变的访问模式。",
    "source": "文档中「缓存的淘汰策略」-「4. ARC（Adaptive Replacement Cache） - 自适应替换缓存策略」部分",
    "hint": "在文档描述的几种淘汰策略中，寻找一种名字里就暗示其具有“自适应”能力的策略。"
  }
];
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        let score = 0;
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            const totalQuestions = quizData.length;
            const answeredCount = userAnswers.filter(a => a !== null).length;
            const wrongCount = answeredCount - correctCount;
            const skippedCount = totalQuestions - answeredCount;
            const accuracy = totalQuestions > 0 ? Math.round((correctCount / totalQuestions) * 100) : 0;
            const errorRate = totalQuestions > 0 ? Math.round((wrongCount / totalQuestions) * 100) : 0;

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                '<h2>答题报告</h2>' +
                '<div class="score-summary">' +
                '<div class="score-card">' +
                '<h3>得分</h3>' +
                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>错误率</h3>' +
                '<p>' + errorRate + '%</p>' +
                '</div>' +
                '<div class="score-card score-details">' +
                '<table>' +
                '<tr><td>正确</td><td>' + correctCount + '</td></tr>' +
                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
                '<button id="review-btn">回顾测验</button>' +
                '<button class="primary" onclick="location.reload()">再试一次</button>' +
                '</div>' +
                '</div>';

            let reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  